About Autonomous cars

Advancement of Autonomous Vehicles in the United States

From 2016, autonomous vehicles have advanced to a new level of growth and development in the United States of America. Due to client demand and desire, many automobile businesses have focused on the invention and development of autonomous vehicles. This has caused technological advancement in the United States of America to reach unprecedented heights. According to Lin (2016), for autonomous automobiles to operate reliably on the roads, they must have decision-making processes that are similar to or better than human decision-making processes. Autonomous vehicles can be self-driven by automatic computer applications which make it easier to avoid some factors that usually affect normal drivers when they are driving (Gerla, Lee, Pau & Lee, 2014). Such variables may include; speed variables, the proliferation of accidents on the roads, naturalistic driving of automobiles, weight and vehicle condition modes, and the state of drivers when they are driving the vehicles on the roads which may include drunkenness.

Benefits of Autonomous Cars

Thus, the development and introduction of autonomous cars will help reduce the number of accidents occurring due to the growth and advancement of better computer control modes of driving. Computers are more prone to stability in operation, and they have limited variability as compared to humans (Levison et al., 2011). Human drivers may be affected by multiple factors while they are driving the vehicles. This may result in accidents, and such cause may be due to lack of concentration of the drivers as a result of mind pressure, effect of stress and emotions. However, autonomous vehicles cannot be affected by such and thus are prone to fewer accidents as compared to human drivers. This discovery that autonomous cars are much better as compared to human drivers has actually increased and revolutionized the technology of automobile industries within the US (Greenblatt & Saxena, 2015).

Market Demand for Autonomous Vehicles

Many people in the US are also waiting to see and use these cars due to their admiration of them. There is always a high demand for autonomous vehicles, and the customers cannot wait to see and buy them so that their lives may be out of danger. The higher demand and market opportunity for the autonomous vehicles have made the automobile companies to try and develop them. Many companies have therefore spent a lot of money to purchase materials that can help to create these vehicles. First and foremost, the General Motors company spent 1 billion investments to acquire cruise automation. Other companies such as Google spent the whole year of 2016 testing the autonomous cars whether they can actually be used on the roads to carry passengers.

Technological Innovations in Autonomous Cars

Moreover, the TESLA Company (TSLA) confirmed succinctly that its cars would be driven by autonomous driving. Similarly, the UBER Company, a taxi passenger firm spent $680 billion on August 2016 to purchase self-driving truck start-up Otto. These inventions were great milestones in the development of autonomous cars in the US. Lastly but not least, automobile vehicle industries decided to introduce a take-over of vehicle building technology by replacing the NXP semi-conductors by the QUALCOMM. This was a more improvised technology that would see the development of autonomous cars jump to another level. As a result, Google, which has been on the forefront of creating autonomous cars decided to increase the number of testing cities for autonomous cars from 1 to 4 others leading to a tremendous milestone of achievement. Thus, the target for automobile industries is that by 2020, everyone should be driving autonomous vehicles in the US and other parts of the world. Currently, the automobile industries in the US are competing to develop autonomous cars.

Automobile Technology and Terminology

Autonomous Cars

Autonomous cars are those types of automobile vehicles which are driven by a computerized non-human mode. They do not have a physical driver like other vehicles do because no human spinning is required. Instead, they are controlled, driven and regulated by a system of computer-aided instructions programmers by the use of technological modes and network data flow. These cars are therefore suited to carry passengers as compared to human drivers because they have the following advantages: First and foremost they reduce accidents caused by human drivers thus they help to save lives of the people (Althoff, 2010). This is because they have a stable control mode that cannot be altered by other factors. Secondly, in case of emotions such as anger and stress, they are not affected thus their focus is mainly on the roads thereby effecting better control utilities (Gerla, Lee, Pau & Lee, 2014).

Locational Data

Locational data is a general term used to signify all the necessary information required and used through a communication network service. It is used to send and relay information concerning the geographical location of an electronic device. There are various types of locational data that can be used to enable effective Geo-location of autonomous vehicles, and these include among the following:

Lasers: They are objects that emit light which can be used to send information via a satellite about the geographical location of the vehicle.

Sensors: A sensor is an electronic object that has the ability to convert physical signal to digital and machine-readable form. The signal is input into the computer to decode the information. Sensors can be essential when used in autonomous vehicles especially when they are used to read speed and weight variables of the car. The computer is able to detect the weight of the car and the speed level by which it is moving. This may be used to analyze and regulate movement variables.

GPS: This is an acronym for Geographical Positioning System. The GPS is an American property that reads the geographical information of all communication networks in the world using satellites such as a transmitter and a receiver of a satellite dish. The GPS is one of the most relevant factors for the development of autonomous cars. It can be used to control the direction of the moving cars by showing a map of the cities and towns by which the vehicle is needed to pass through. The GPS can also show the auto driver the various vehicles ahead and behind it thus providing very accurate data which is used to control the autonomous moving vehicle.

Software Requirements

Laptop: This is an important gadget that is used to key in driving instructions to the auto-drive so as to instruct the auto driving mode to take turns. Every car owner must have a laptop so that he may be able to control the speed and other driving modes.

Color-coded coordination: This is a type of color patterns that can be applied in auto-driving control modes and also in the traffic control utilities.

Object classification: This can be defined as the process where an object is assigned semantic labels so that it can be used to enhance computer vision. This technology has various applications, but the relevant application is where it is used for localization which is discussed above in this paper. This is the process of monitoring the modes of driving by the human drivers. In autonomous vehicles, naturalistic driving has been used to survey and keeps a record of various modes including the speed of the moving vehicle at every hour, the total weight of the vehicle when it is fully loaded with both humans and luggage and comparing it with the weight of the vehicle when it is empty. The information is then used to analyze the causes of accidents caused by the moving vehicle. Finally, developer feedback is a system of collecting feedback from the customers by the autonomous vehicle industries.

Research of Three Best Autonomous Vehicles

Audi Car

This is an autonomous car that is a representation of the E-tron Quattro technology. The car is retrofitted with radar technology which is ideal for any autonomous moving vehicle in terms of security. It is also fitted with multiple video cameras that are used to send information to the control center on the laptop. The vehicle has the ability to scan the surrounding environment and thus identifying everything that requires attention. It has a hyper mapping utility that is able to scan its geographical location wherever it passes through hence has the ability to inform the owner about various moving vehicles coming around. Finally, the car has got a cloud-based wide communication system which is fast and very convenient to enhance the communication between the owner and the auto driving mechanism. Urgent information is retrieved as fast as possible providing the need for necessary adjustments during driving.

The Volvo S60

Another car that is autonomously driven is referred to as the Volvo S60. This vehicle is classified into a level 3 autonomous vehicle that makes use of radar, lasers, sensors, and GPS to navigate the road using robotic driving mechanism. Previously, the car has had several challenges which have been targeted and are being handled. First and foremost, they are costly, and the materials needed to manufacture this vehicle are not cheap, thus making it hard to create many cars of the same kind. Secondly, there have been problems with the GPS and sensors which have sometimes relayed wrong data and information. The future prediction for these cars is that it will be free from GPS errors due to the introduction of amplified satellite technologies and use of wireless network connections (Greenblatt & Saxena, 2015).

Google Self-Driving Car

It is another type of autonomous vehicle and it has successfully hit market demands. There have been several versions of the Google cars which have been tested and were confirmed to cause no accident even after traveling 300,000 miles of autonomous driving. The current latest versions have no brake pedals, steering wheel, and gas pedal. However, these cars' computers are occasionally compromised, thus affecting the communication system between cars. This may result in an accident and other related problems. Fortunately, the government of the US has accepted to allow public testing of the autonomous vehicles for Google in four cities within the United States of America. This has provided an opportunity for Google to carry out thorough tests to check for more errors and fixing them.

Conclusion

In conclusion, autonomous cars have a great advantage to the people in society. First and foremost, they are able to reduce the number of accidents since they are driven by automatic computer mechanisms. The absence of drivers makes it possible to avoid some human factors which may result in dangerous driving including emotions, stress, use of alcohol, and drug abuse. They are less cost-efficient, especially due to no need for human labor.

On the contrary, the society is going to suffer from the lack of jobs due to the replacement of human drivers with auto-driving mechanisms. There will also be a lack of market for normal cars hence their industries are likely to close down. This will affect the economy of these industries hence the economy of the state will drop at some point in time. Some applications of autonomous vehicles besides in the transport industry can be applied in the military operations whereby autonomous war vehicles can be used to transport weapons from the military base taking it to the soldiers wherever they are located. Autonomous cars can also be used to carry soldiers on the back of the trucks thus the enemies may not be able to shoot human drivers. In conclusion, autonomous cars have great relevance and importance in society.

References

Gerla, M., Lee, E. K., Pau, G., & Lee, U. (2014, March). Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds. In Internet of Things (WF-IoT), 2014 IEEE World Forum on (pp. 241-246). IEEE.

Greenblatt, J. B., & Saxena, S. (2015). Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles. Nature climate change, 5(9), 860-863.

Lin, P. (2016). Why ethics matters for autonomous cars. In Autonomous Driving (pp. 69-85). Springer Berlin Heidelberg.

Levinson, J., Askeland, J., Becker, J., Dolson, J., Held, D., Kammel, S., ... & Sokolsky, M. (2011, June). Towards fully autonomous driving: Systems and algorithms. In Intelligent Vehicles Symposium (IV), 2011 IEEE (pp. 163-168). IEEE.

Althoff, M. (2010). Reachability analysis and its application to the safety assessment of autonomous cars. Technische Universitt Mnchen.